Striim™, Inc. announced that it has launched version 3.7 of its end-to-end real-time data integration and streaming analytics platform. Driven by customer requirements and demand, this release focuses on facilitating real-time, hybrid Cloud integration, and simplifying the management of applications running on streaming data.
Highlights of this latest version include several hybrid cloud integrations with Microsoft® data solutions running on Azure®, and enhanced application-to-database management through dynamic schema evolution.
Many of our customers are increasingly adopting real-time cloud-based solutions. Striim enables them to rapidly deploy hybrid cloud solutions with continuous data movement from on-premise systems to the cloud. With this release, Striim is the only vendor to offer multiple metered solutions for streaming data integration and analytics on the Azure Marketplace,” said Alok Pareek, founder and EVP of Products at Striim. “Adding to this numerous platform enhancements such as support for schema evolution and DDL propagation, we continue to up the ante on streaming data management.”
Hybrid Cloud Integration
Striim has launched two new solutions on the Microsoft Azure Marketplace. For all solutions, Striim’s wizard-based design interface speeds deployment and time-to-value for its Azure-based customers.
Striim for Real-Time Integration to SQL Database simplifies the real-time collection and movement of data from a wide variety of on-premise sources, including enterprise databases via Change Data Capture (CDC), into Azure SQL Database.
While the data is streaming, Striim enables in-flight processing, (i.e., filtering, transformations, enrichment) before the data lands in SQL Database, providing full context for any downstream operations, such as reporting, and hybrid transactional and analytical processing.
The Striim for Real-Time Integration to SQL Database solution set is available here on the Microsoft Azure Marketplace.
Striim for Real-Time Integration to Azure Storage provides the same real-time collection and movement of data from a wide variety of on-premise sources, including enterprise databases via log-based Change Data Capture (CDC), into Azure Storage.
This Striim solution enables Azure Blob Storage and Azure File Storage customers to quickly build streaming data pipelines, choosing their desired data latency (real-time, micro batch, or batch) and enrich with additional context. These pipelines can support any application or Big Data analytics solution, including Azure HDInsight®, that uses Azure Storage.
The Striim for Real-Time Integration to Azure Storage solution set is available here on the Microsoft Azure Marketplace.
Next Up
This latest release of the Striim platform also includes integrations with Microsoft HDInsight on Azure, and Microsoft Event Hub on Azure, both of which will soon be available on the Microsoft Azure Marketplace.
Schema Evolution for Continuously Running Applications
One of the most difficult challenges of managing enterprise applications that are running against continuous data streams is the application downtime required to make changes to the schema. Striim has solved this with the introduction of Schema Evolution.
Schema Evolution enables business to evolve their schema or make DDL changes without affecting the related applications, ensuring the integration pipeline continues uninterrupted. The new Schema Evolution feature also allows for auditing of schema changes for lineage and compliance.
Schema Evolution bolsters productivity, reduces application downtime due to DDL changes, and increases manageability of continuously running applications.
Additional Product Enhancements
Striim’s Exactly-Once Processing has been enhanced for additional target components.
New Interval Reporting of streaming data, combined with existing stream introspection capabilities, help customers better troubleshoot problems and report on business intervals.
Replication enhancements include new Era Tracking functionality that allows companies to track streaming metadata into eras to support recovery through schema evolution.
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